Method, apparatus and system for passive infrared sensor framework

ABSTRACT

A method includes detecting, with a passive infrared sensor (PIR), a level of infrared radiation in a field of view (FOV) of the PIR, generating a signal based on detected levels over a period of time, the signal having values that exhibit a change in the detected levels, extracting a local feature from a sample of the signal, wherein the local feature indicates a probability that a human in the FOV caused the change in the detected levels, extracting a global feature from the sample of the signal, wherein the global feature indicates a probability that an environmental radiation source caused the change in the detected levels, determining a score based on the local feature and the global feature, and determining that a human motion has been detected in the FOV based on the score.

RELATED APPLICATIONS

This application is a continuation application from U.S. ApplicationSer. No. 15/430,256 filed on Feb. 10, 2017 which is U.S. Pat. No.10,712,204 issued on DATE.

BACKGROUND

Passive infrared (PIR) sensors can detect human motion by measuringinfrared variations in a scene. Applications for PIR sensing include,for example, automatic indoor light switching from occupancy, intrusiondetection for security, etc. In practice, many noise sources, such asinternal device noise or external interference from heaters, cangenerate strong signals comparable to those of humans, and can falselytrigger the PIR device for human motion.

BRIEF SUMMARY

According to an embodiment of the disclosed subject matter, a methodincludes detecting, with a passive infrared sensor (PIR), a level ofinfrared radiation in a field of view (FOV) of the PIR, generating asignal based on detected levels over a period of time, the signal havingvalues that exhibit a change in the detected levels, extracting a localfeature from a sample of the signal, wherein the local feature indicatesa probability that a human in the FOV caused the change in the detectedlevels, extracting a global feature from the sample of the signal,wherein the global feature indicates a probability that an environmentalradiation source caused the change in the detected levels, determining ascore based on the local feature and the global feature, and determiningthat a human motion has been detected in the FOV based on the score.

According to an embodiment of the disclosed subject matter, a passiveinfrared (PIR) sensor device includes a plurality of radiation sensitiveelements that detect a radiation level in a field of view (FOV) andgenerate charges based on the detected radiation level, a circuit thatreceives the generated charges and generates a signal based on thecharges, and a processor that receives the signal and extracts a localfeature and a global feature from a sample of the signal, the localfeature indicating a probability that a human in the FOV caused thechange in the detected levels and the global feature indicating aprobability that an environmental radiation source caused the change inthe detected levels, wherein the processor is configured to determine ascore based on the local feature and the global feature and to determinethat a human motion has been detected in the FOV based on the score.

According to an embodiment of the disclosed subject matter, a systemincludes a network, a controller configured to transmit and receive datathrough the network, and at least one passive infrared (PIR) sensordevice configured to transmit data to the controller through thenetwork, the PIR sensor device including a plurality of radiationsensitive elements that detect a radiation level in a field of view(FOV) and generate charges based on the detected radiation level, acircuit that receives the generated charges and generates a signal basedon the charges, and a processor that receives the signal and extracts alocal feature and a global feature from a sample of the signal, thelocal feature indicating a probability that a human in the FOV causedthe change in the detected levels and the global feature indicating aprobability that an environmental radiation source caused the change inthe detected levels, wherein the processor is configured to determine ascore based on the local feature and the global feature and to determinethat a human motion has been detected in the FOV based on the score.

According to an embodiment of the disclosed subject matter, means fordetecting, with a passive infrared sensor (PIR), a level of infraredradiation in a field of view (FOV) of the PIR, generating a signal basedon detected levels over a period of time, the signal having values thatexhibit a change in the detected levels, extracting a local feature froma sample of the signal, wherein the local feature indicates aprobability that a human in the FOV caused the change in the detectedlevels, extracting a global feature from the sample of the signal,wherein the global feature indicates a probability that an environmentalradiation source caused the change in the detected levels, determining ascore based on the local feature and the global feature, determiningthat a human motion has been detected in the FOV based on the score, areprovided.

Additional features, advantages, and embodiments of the disclosedsubject matter may be set forth or apparent from consideration of thefollowing detailed description, drawings, and claims. Moreover, it is tobe understood that both the foregoing summary and the following detaileddescription are illustrative and are intended to provide furtherexplanation without limiting the scope of the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the disclosed subject matter, are incorporated in andconstitute a part of this specification. The drawings also illustrateembodiments of the disclosed subject matter and together with thedetailed description serve to explain the principles of embodiments ofthe disclosed subject matter. No attempt is made to show structuraldetails in more detail than may be necessary for a fundamentalunderstanding of the disclosed subject matter and various ways in whichit may be practiced.

FIG. 1A shows a passive infrared (PIR) sensor device, according to anembodiment of the disclosed subject matter.

FIG. 1B shows a PIR sensor, field of view (FOV), and detection signalaccording to an embodiment of the disclosed subject matter.

FIG. 2 shows a signal readout of a PIR sensor disposed in a room over aperiod of time with no environmental radiation sources.

FIG. 3 shows a signal readout of a PIR sensor disposed in a room over aperiod of time with a heater present.

FIG. 4 shows a flowchart of an implementation of the disclosedframework, according to an embodiment of the disclosed subject matter.

FIG. 5 shows a graph of test samples against corresponding scores usinga conventional absolute value scoring algorithm.

FIG. 6 shows a graph of test samples against corresponding scores usingthe disclosed framework, according to an embodiment of the disclosedsubject matter.

FIG. 7 shows an example of a sensor network, according to an embodimentof the disclosed subject matter.

DETAILED DESCRIPTION

Various aspects or features of this disclosure are described withreference to the drawings, wherein like reference numerals are used torefer to like elements throughout. In this specification, numerousdetails are set forth in order to provide a thorough understanding ofthis disclosure. It should be understood, however, that certain aspectsof disclosure may be practiced without these specific details, or withother methods, components, materials, etc. In other instances,well-known structures and devices are shown in block diagram form tofacilitate describing the subject disclosure.

A passive infrared (PIR) sensor framework is disclosed herein thatreduces the negative effects of environmental noise on accuracy, has alow computational complexity, and improves battery life.

Generally, a PIR sensor can detect infrared radiation emanating from asource within the sensor's field of view (FOV). FIG. 1A shows a PIRsensor 100. The sensor 100 can include two radiation-sensitive elements110, 120 connected to generate opposite charges when hit by incidentradiation. For example, 110 can produce a positive charge and 120 canproduce a negative charge. The sensor 100 includes circuitry 130 totransform the charges into a signal voltage and process the signal. Forexample, circuitry 130 can include an amplifier 140 to amplify a signal,a buffer 150 to store signal samples, a processor 160 to process thesignal samples, a communications interface 165 to transmit data andnotifications, and a battery 170 to power the sensor 100.

The sensor 100 can be disposed in a housing (not shown) including aFresnel lens/array 180 positioned to function as a focusing device thatpartitions the sensor's FOV into a one or more distributed zones.

FIG. 1B shows the sensor installed, for example, in a home securitysystem. As shown in FIG. 1B the Fresnel lens focuses radiation from theone or more distributed zones 182, 184 onto the radiation-sensitiveelements 110, 120 of the sensor 100. When an individual enters thesensor 100 FOV and passes through the zones 182, 184, the sensor 100generates an output signal 186.

Illustrative circuits, devices, and the like may be described herein ingeneral terms with respect to interaction between severalcomponents/blocks. A person of ordinary skill in the art wouldappreciate that this description is not limiting in that such circuitsand components/blocks can include those components or specifiedsub-components, some of the specified components or sub-components,and/or additional components, according to various permutations andcombinations of the foregoing. Sub-components can also be implemented ascomponents communicatively coupled to other components rather thanincluded within parent components (hierarchical). Additionally, itshould be noted that one or more components may be combined into asingle component providing aggregate functionality or divided intoseveral separate sub-components, and any one or more middle layers, suchas a management layer, may be provided to communicatively couple to suchsub-components in order to provide integrated functionality. Componentsdescribed herein may also interact with one or more other components notspecifically described herein but known by those of ordinary skill inthe art.

The PIR sensor 100 can be used to detect motion, for example, as part ofa security system disposed in or around a premises. However, aconventional PIR sensor framework often generates false alerts based onnoise, such as interference from external radiation sources.Furthermore, a PIR sensor that is part of a security system, e.g., acommercial home security system, may have design constraints that limitthe size of the sensor, the battery power and the computation power ofthe device.

Signal to noise ratio (SNR) is a significant problem in PIR sensors thathave limited size, battery power and computational power. Generally, allother factors being equal, the smaller the sensor, the lower the SNR andconsequently the greater the potential for signal noise to result infalse detections. The likelihood of a false detection increases furtherstill with the introduction of environmental radiation sources.

FIG. 2 shows a signal readout of a PIR sensor disposed in a room over aperiod of time with no environmental radiation sources. From time A to Bthe room is empty. At time B an individual enters the FOV of the PIRsensor. A conventional PIR frameworks detects the motion of theindividual by filtering the signal to remove noise and using signalthresholding to determine whether human detection has occurred. Usingthis method, human detection is determined to have occurred at time Bwhen the signal crosses a threshold.

A disadvantage of this approach is that in many practical installationscenarios (e.g., a highly-mounted PIR sensor device near an air vent orwith an air vent in the FOV) the heat interference signal strength iscomparable to that of human motion, and thus introduces many falsepositive alarms.

For example, FIG. 3 shows a signal readout of a PIR sensor disposed in aroom over a period of time with a heater present. From time A to B theroom is empty and the heater is turned off. At time B an individualenters the FOV of the PIR sensor. At time C the heater is turned on.Using basic conventional thresholding techniques, human detection isdetermined to have occurred at times B and C, although no human ispresent at time C.

A conventional solution to this problem is to use device temperatureinformation to detect heaters and attempt to use offsets to avoidworst-case heat interference scenarios. However, because there is noperfect correlation between PIR signal variations and device temperaturewhen considering large device-to-device variations, temperature-basedcontrol methods have limited advantages.

The disclosed subject matter solves the problem of false detectiondecisions triggered by environmental radiation by using a data-drivenstatistical framework that computes useful human motion features in thePIR signal domain itself, and thus is not affected by device variationsand is robust for human motion detection, even in worst-caseenvironmental heat scenarios.

FIG. 4 shows a flowchart 400 of an implementation of the disclosedframework. At operation 410 the PIR sensor detects radiation in its FOVand generates a signal at operation 420. A sample of the signal isprocessed by a processor to extract a local feature c_(local) atoperation 430 and a global feature c_(global) at operation 440. Atoperation 450 the processor uses a scoring function to determine a scorebased on the local feature and the global feature. Based on the score,the processor determines whether a human is in the FOV at operation 460.

The local feature indicates a probability that a human is in the FOV,i.e., that transient human motion is the source that caused theradiation level indicated by the signal sample. The global featureindicates a probability that an environmental source, as opposed to ahuman source, is the cause for the radiation level indicated by thesignal sample. The scoring function classifies the signal sample andincludes predetermined parameters that can be tuned for accuracy, forexample, via dataset training. Similarly, the parameters in theaforementioned feature extraction operations can be refined in offlinetraining, using a labeled motion and non-motion dataset. The trainingdataset can include data from internal controlled testing runs, forexample, PIR sensors disposed in actual houses.

In the disclosed framework, at each frame the PIR sensor records scenetemperature based on the Fresnel lens patterns (spatial gradients). APIR sensor value x at frame k can be expressed as:

$\begin{matrix}{{x_{k} = {{\sum_{i}{x_{k}^{+}(i)}} - {\sum\limits_{j}x_{k}^{-}}}}(j)} & \left( {{Eq}.\mspace{14mu} 1} \right)\end{matrix}$Given PIR sensor value readings {x_(k)}k=1, 2, 3 . . . , the disclosedframework can detect human motion in real time at a low computationalcost and with reduced false alerts due to environmental noise.

The local feature c_(local) can be extracted (i.e., operation 430 ofFIG. 4) from the signal sample x_(k) using a computationally low-cost,lightweight function. In one implementation, the function can be definedas:c _(local)(x _(k))=|x _(k)|  (Eq. 2)This function tracks the absolute deviation of the signal sample, underthe notion that a deviation approaching a threshold predetermined toapproximate human movement serves as an indication that human motioncaused the sample to cross the threshold. Therefore, c_(local) indicatesa probability of immediate human intrusion into the FOV. The Eq. 2function has a low computational cost, which factors into otheradvantages, such as battery life, as will be discussed later.

In another implementation, the c_(local) extraction function can bedefined as:c _(local)(x _(k))=Ax _(k) +b  (Eq. 3)where A and b are parameters determined to achieve linear dimensionalityreduction via, e.g., PCA or sparse encoding. Eq. 3 can achieve higheraccuracy than Eq. 2 in some instances, but has a higher computationalcost.

The global feature c_(global) can be extracted (i.e., operation 440 ofFIG. 4) from the signal sample x_(k) using a relatively low-costfunction. In one implementation, the function can be defined as:c _(global)(x _(k))=(1−∈)x′+∈|x _(k) −x _(k−1)|  (Eq. 4)where ∈ is a feature parameter determined through training on a datasetto achieve an slow total variation having a desired accuracy level andx′ is a rolling average that is updated after every frame. In thisimplementation, c_(global) becomes relatively high when radiation jitteris sustained over a long period of time. A sustained jitter generallymay indicate that the source of radiation is a persistent environmentalpresence rather than a transient intruder, therefore, c_(global)indicates a probability of environmental noise.

In another implementation, the c_(global) extraction function can bedefined as:c _(global)(x _(k))=(1−∈)x′+∈|x _(k)|  (Eq. 5)In this case, c_(global) is a slow follower of absolute deviation. Thisfunction generally may produce less accurate results than Eq. 4.However, it generally has a lower computational cost and still resultsin a c_(global) that indicates a probability of sustained environmentalnoise.

The disclosed framework is also capable of using the global feature toidentify the presence of an environmental aggressor. For example, whenc_(global) exceeds a predetermined threshold R_(cg) for a given lengthof time T_(cg), the processor can be configured to send a notificationto the user to adjust the position of the sensor or adjust theenvironment. That is, the framework can determine that a current layouthas a likelihood of eventually producing a false alarm.

The scoring function (i.e., operation 450 of FIG. 4) can be based on aclassifier for separating motion indicators (c_(local)) from non-motionindicators (c_(global)). In one implementation, a linear classifier,such as support vector machines (SVM) can be used on the feature set toclassify input. Using SVM, a linear scoring function ƒ({right arrow over(σ)}) can be defined as:

$\begin{matrix}{{f\left( \overset{\rightarrow}{\sigma} \right)} = {a_{0} + {\sum\limits_{k - 1}^{n}{a_{k}{\overset{\rightarrow}{\sigma}}_{k}}}}} & \left( {{Eq}.\mspace{14mu} 7} \right)\end{matrix}$where a_(n) is an SVM coefficient determined by training on the datasetand {right arrow over (σ)} is a sample vector determined based on theextracted features c_(global) and c_(local). In one implementation thesample vector {right arrow over (σ)} can be computed, for example, byconcatenating c_(global) and c_(local).

The sample vector may be provided to a scoring function to determine ascore, which can then be compared to a predetermined threshold R todetermine whether human movement has been detected in the FOV (i.e.,operation 460 of FIG. 4).

FIG. 5 shows a graph of test samples against corresponding scores usinga conventional absolute value scoring algorithm. The sample set wascollected over a period of time from a PIR sensor in an environment witha heater turned on in the sensor's FOV, but no human motion. Incontrast, the illustrative conventional algorithm determines humanmotion has occurred when the score exceeds threshold R. As can be seen,the conventional algorithm results in several false detection outcomes.

FIG. 6 shows a graph of test samples against corresponding scores usingthe disclosed framework of FIG. 4. The sample set was collected over aperiod of time from a PIR sensor in the same environment with a heaterturned on in the sensor's FOV, with no human motion. This disclosedframework successfully recognizes that no human motion has occurredwithin the FOV. Unlike the conventional method, the disclosed frameworksenses an environmental aggressor in the FOV and negatively corrects thescore such that no false detections occur. The score remains below thethreshold R.

In all of the implementations described above, when using an SVMclassifier, the threshold R, SVM coefficients and feature parameter E,can be determined and/or refined by testing and labeling datasetsobtained under controlled conditions. These values can be selected withdifferent goals, for example, to improve accuracy, to decrease mean timeto failure (MTTF) for the PIR sensor, or to increase the effective FOV.Generally, lowering threshold R increases the FOV, but lowers MTTF.Testing can be employed to determine a value for R that achieves adesired balance.

The disclosed framework is different from conventional approaches inthat it functionally recognizes the statistics of motion and non-motionPIR signals and derives effective ways of separating the two signal setsin a data-driven way, thereby achieving high motion detection accuracywith low computational cost. For example, since the disclosed featureextraction operations are low-cost (in the worst case, a singlematrix-vector multiplication even when the matrix is large) and thedisclosed classification operation is low-cost (e.g., one dot productfor SVM), the full framework pipeline can run at an acquisition framerate of 10 Hz without noticeable battery power reduction.

In practice, the disclosed framework can achieve accurate results with acomputational cost as low as 9 elementary arithmetic operations perframe, which can meet low-cost design restraints. In one implementation,a PIR sensor 100 can include a low-power processor that can compute amaximum of 10 elementary arithmetic operations per cycle. In one testimplementation, keeping the number of operations under 10 resulted inthe device having a determined active battery operation of at least 2years. In addition to the framework only needing elementary arithmeticoperations, no floating point numbers are required, further lowering thecomputational cost.

The number of instructions per cycle can be so low as to not exceed 6instructions, where each instruction can be considered to be equivalentto a line of code or a step in an algorithm that may include one or moreoperations. With all of the above described low-cost features, thebattery life of the PIR sensor has been shown in testing to be greatlyimproved over conventional techniques. For example,

In some configurations, the disclosed PIR sensor framework can beimplemented as a set of computer-readable instructions stored on acomputer-readable storage medium to be implemented by a general-purposeprocessor, which may transform the general-purpose processor or a devicecontaining the general-purpose processor into a special-purpose deviceconfigured to implement or carry out the instructions. Embodiments canbe implemented using hardware that can include a processor, such as ageneral purpose microprocessor and/or an Application Specific IntegratedCircuit (ASIC) that embodies all or part of the techniques according toembodiments of the disclosed subject matter in hardware and/or firmware.The processor may be coupled to memory, such as a buffer, register, RAM,ROM, flash memory, hard disk or other device capable of storingelectronic information. The memory may store instructions adapted to beexecuted by the processor to perform the PIR sensor framework techniquesaccording to embodiments of the disclosed subject matter.

PIR sensors including the framework as disclosed herein can also operatewithin a communication network, such as a conventional wireless network,and/or a sensor-specific network through which sensors may communicatewith one another and/or with dedicated other devices. In someconfigurations one or more disclosed PIR sensors can provide informationto one or more other sensors, to a central controller, or to any otherdevice capable of communicating on a network with the one or moresensors. A central controller can be general- or special-purpose. Forexample, one type of central controller is a home automation networkthat collects and analyzes data from one or more sensors within thehome. Another example of a central controller is a special-purposecontroller that is dedicated to a subset of functions, such as asecurity controller that collects and analyzes sensor data primarily orexclusively as it relates to various security considerations for alocation. A central controller can be located locally with respect tothe sensors with which it communicates and from which it obtains sensordata, such as in the case where it is positioned within a home thatincludes a home automation and/or sensor network. Alternatively or inaddition, a central controller as disclosed herein can be remote fromthe sensors, such as where the central controller is implemented as acloud-based system that communicates with multiple sensors, which can belocated at multiple locations and can be local or remote with respect toone another.

FIG. 7 shows an example of a sensor network as disclosed herein, whichcan be implemented over any suitable wired and/or wireless communicationnetworks. One or more sensors 71, 72, for example PIR sensors includingthe disclosed framework, may communicate via a local network 70, such asa Wi-Fi or other suitable network, with each other and/or with acontroller 73. The controller can be a general- or special-purposecomputer. The controller can, for example, receive, aggregate, and/oranalyze information received from the sensors 71, 72, such as detectionalerts, notifications, or environmental information. The sensors 71, 72and the controller 73 can be located locally to one another, such aswithin a single dwelling, office space, building, room, or the like, orthey can be remote from each other, such as where the controller 73 isimplemented in a remote system 74 such as a cloud-based reporting and/oranalysis system. Alternatively or in addition, sensors can communicatedirectly with a remote system 74. The remote system 74 can, for example,aggregate data from multiple locations, provide instruction, softwareupdates, and/or aggregated data to a controller 73 and/or sensors 71,72.

In some configurations, a remote system 74 can aggregate data frommultiple locations, such as multiple buildings, multi-residentbuildings, individual residences within a neighborhood, multipleneighborhoods, and the like. In general, multiple sensor/controllersystems can provide information to the remote system 74. The multiplesystems can provide data directly from one or more sensors as previouslydescribed, or the data can be aggregated and/or analyzed by localcontrollers such as the controller 73, which then communicates with theremote system 74. The remote system can aggregate and analyze the datafrom multiple locations, and can provide aggregate results to eachlocation. For example, the remote system 74 can examine larger regionsfor common sensor data or trends in sensor data, and provide informationon the identified commonality or environmental data trends to each localsystem.

In situations in which the systems discussed here collect personalinformation about users, or may make use of personal information, theusers may be provided with an opportunity to control whether programs orfeatures collect user information (e.g., information about a user'ssocial network, social actions or activities, profession, a user'spreferences, or a user's current location), or to control whether and/orhow to receive content from the content server that may be more relevantto the user. In addition, certain data may be treated in one or moreways before it is stored or used, so that personally identifiableinformation is removed. For example, specific information about a user'sresidence may be treated so that no personally identifiable informationcan be determined for the user, or a user's geographic location may begeneralized where location information is obtained (such as to a city,ZIP code, or state level), so that a particular location of a usercannot be determined. As another example, systems disclosed herein mayallow a user to restrict the information collected by those systems toapplications specific to the user, such as by disabling or limiting theextent to which such information is aggregated or used in analysis withother information from other users. Thus, the user may have control overhow information is collected about the user and used by a system asdisclosed herein.

Some portions of the detailed description are presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are commonly used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities such as, for example, measured levels of IR radiation withina specific physical space. Usually, though not necessarily, thesequantities take the form of electrical or magnetic signals capable ofbeing stored, transferred, combined, compared and otherwise manipulated.It has proven convenient at times, principally for reasons of commonusage, to refer to these signals as bits, values, elements, symbols,characters, terms, numbers, or the like.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments and implementations.However, the illustrative discussions above are not intended to beexhaustive or to limit embodiments of the disclosed subject matter tothe precise forms disclosed. Many modifications and variations arepossible in view of the above teachings. The embodiments were chosen anddescribed in order to explain the principles of embodiments of thedisclosed subject matter and their practical applications, to therebyenable others skilled in the art to utilize those embodiments as well asvarious embodiments with various modifications as may be suited to theparticular use contemplated.

The invention claimed is:
 1. A method comprising: detecting, with apassive infrared sensor (PIR), levels of infrared radiation in a fieldof view (FOV) of the PIR; generating a signal based on the levels ofinfrared radiation detected with the PIR over a period of time, thesignal having values that exhibit a change in a level of infraredradiation as detected with the PIR; extracting a local feature from asample of the signal by tracking an absolute deviation of the sample ofthe signal relative to a threshold predetermined to approximate humanmovement, wherein the local feature indicates a first probability that ahuman in the FOV caused the change in the detected levels of infraredradiation; extracting a global feature from the sample of the signal bya product of a feature parameter determined through training on adataset to achieve a slow total variation and a rolling average valueupdated after every frame of the PIR, wherein the global featureindicates a second probability that an environmental radiation sourcecaused the change in the detected levels of infrared radiation;determining a score based on the local feature and the global feature;and determining that a human motion has been detected in the FOV basedon the score.
 2. The method of claim 1, wherein the local feature(c_(local)) of a k^(th) sample of the signal (x) is determined by thefunction:c _(local)(x _(k))=|x _(k)|.
 3. The method of claim 1, wherein the localfeature (c_(local)) of a k^(th) sample of the signal (x) is determinedby the function:c _(local)(x _(k))=Ax _(k)+b where A and b are learned parametersdetermined, through training on a dataset of prior PIR values, toachieve linear dimensionality reduction.
 4. The method of claim 1,wherein the global feature (c_(global)) of a k^(th) sample of the signal(x) is determined by the function:c _(global)(x _(k))=(1−ε)x′+ε|x_(k)−x_(k−1)| where ε is a learnedparameter determined through training on a dataset of prior PIR values,and where x′ is a rolling average that is updated after every frame. 5.The method of claim 1, wherein the global feature (c_(global)) of ak^(th) sample of the signal (x) is determined by the function:c _(global)(x _(k))=(1−ε)x′+ε|x _(k)| where ε is a learned parameterdetermined through training on a dataset of prior PIR values, and wherex′ is a rolling average that is updated after every frame.
 6. The methodof claim 1, wherein the classifier is a linear support vector machine(SVM).
 7. The method of claim 6, wherein a number of elementaryoperations executed per sample of the signal to detect human motion isless than
 10. 8. The method of claim 6, wherein a number of instructionsper signal sample is less than
 7. 9. The method of claim 1, furthercomprising transmitting a notification indicating that the FOV of thePIR is not optimal for human detection when the global feature remainsabove a threshold value for a predetermined length of time.
 10. Apassive infrared (PIR) sensor device, comprising: a plurality ofradiation sensitive elements that detect radiation levels in a field ofview (FOV) and generate charges based on the radiation levels; a circuitthat receives the generated charges and generates a single output signalbased on the charges; and a processor that receives the signal andextracts a local feature from a sample of the signal by tracking anabsolute deviation of the sample of the signal relative to a thresholdpredetermined to approximate human movement and a global feature fromthe sample of the signal by a product of a feature parameter determinedthrough training on a dataset to achieve a slow total variation and arolling average value updated after every frame of the PIR, the localfeature indicating a first probability that a human in the FOV caused achange in the detected radiation levels and the global featureindicating a second probability that an environmental radiation sourcein the FOV caused the change in the detected radiation levels, whereinthe processor is configured to determine a score based on the globalfeature and the local feature, and to determine that a human motion hasbeen detected in the FOV based on the score.
 11. The device of claim 10,wherein the processer extracts the local feature (c_(local)) of a k^(th)sample of the signal (x) by executing the function:c _(local)(x _(k))=|x _(k)|.
 12. The device of claim 10, wherein theprocesser extracts the local feature (c_(local))of a k^(th) sample ofthe signal (x) by executing the function:c _(local)(x _(k))=Ax _(k) +b where A and b are learned parametersdetermined, through training on a dataset, to achieve lineardimensionality reduction.
 13. The device of claim 10, wherein theprocesser extracts the global feature (c_(global)) of a k^(th) sample ofthe signal (x) by executing the function:c _(global)(x _(k))=(1−ε)x′+ε|x _(k)−x _(k−1)| where ε is a learnedparameter determined through training on a dataset, and where x′ is arolling average that is updated after every frame.
 14. The device ofclaim 10, wherein the processer extracts the global feature (c_(global))of a k^(th) sample of the signal (x) by executing the function:c _(global)(x _(k))=(1−ε)x′+ε|x _(k)| where ε is a learned parameterdetermined through training on a dataset, and where x′ is a rollingaverage that is updated after every frame.
 15. The device of claim 10,wherein the classifier is a linear support vector machine (SVM).
 16. Thedevice of claim 15, wherein a number of elementary operations executedby the processor per sample of the signal to detect human motion is lessthan
 10. 17. The device of claim 15, wherein a number of instructionsexecuted by the processor per signal sample is less than
 7. 18. Thedevice of claim 10, further comprising: a communication interfaceconfigured to transmit a notification indicating that the environmentwithin the FOV is not optimal for human detection when the processordetermines that the global feature remains above a threshold value for apredetermined length of time.
 19. A system, comprising: a network; acontroller configured to transmit and receive data through the network;and at least one passive infrared (PIR) sensor device configured totransmit data to the controller through the network, the PIR sensordevice comprising: a plurality of radiation sensitive elements thatdetect radiation levels in a field of view (FOV) and generate chargesbased on the detected radiation levels; a circuit that receives thegenerated charges and generates a single output signal based on thecharges; and a processor that receives the signal and extracts a localfeature from a sample of the signal by tracking an absolute deviation ofthe sample of the signal relative to a threshold predetermined toapproximate human movement and a global feature from the sample of thesignal by a product of a feature parameter determined through trainingon a dataset to achieve a slow total variation and a rolling averagevalue updated after every frame of the PIR, the local feature indicatinga first probability that a human in the FOV caused the change in thedetected radiation levels and the global feature indicating a secondprobability that an environmental radiation source caused the change inthe detected radiation levels, wherein the processor is configured todetermine a score based on the global feature and the local feature andto determine that a human motion has been detected in the FOV based onthe score.
 20. The system of claim 19, wherein the processer extractsthe local feature (c_(local)) of a k^(th) sample of the signal (x) byexecuting the function:c _(local)(x_(k))=|x _(k)|
 21. The system of claim 19, wherein theprocesser extracts the global feature (c_(global)) of a k^(th) sample ofthe signal (x) by executing the function:c _(global)(x _(k))=(1−ε)x′+ε|x _(k)−x _(k−1|) where ε is a learnedparameter determined through training on a dataset, and where x′ is arolling average that is updated after every frame.
 22. The system ofclaim 19, wherein the classifier is a linear support vector machine(SVM).